Yanbang Wang
- Artificial Intelligence
- Atomic and Molecular Physics, and Optics
- Computer Vision and Pattern Recognition
- Soil Science
- Statistical and Nonlinear Physics
- Topics
- Advanced Graph Neural Networks (5 papers)Quantum optics and atomic interactions (3 papers)Cold Atom Physics and Bose-Einstein Condensates (3 papers)
- Partner nations
- ChinaUnited StatesCzechia
In The Last Decade
Yanbang Wang
14 papers receiving 178 citations
Peers
Comparison fields: 5 of 59
- Artificial Intelligence 60
- Atomic and Molecular Physics, and Optics 50
- Computer Vision and Pattern Recognition 35
- Soil Science 25
- Statistical and Nonlinear Physics 21
Countries citing papers authored by Yanbang Wang
This map shows the geographic impact of Yanbang Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yanbang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yanbang Wang more than expected).
Fields of papers citing papers by Yanbang Wang
This network shows the impact of papers produced by Yanbang Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yanbang Wang. The network helps show where Yanbang Wang may publish in the future.
Co-authorship network of co-authors of Yanbang Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yanbang Wang. A scholar is included among the top collaborators of Yanbang Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yanbang Wang. Yanbang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 15 | |
| 3 | Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks | 7 |
| 4 | 15 | |
| 5 | Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning | 6 |
| 6 | 49 | |
| 7 | Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning | 5 |
| 8 | 19 | |
| 9 | Distance Encoding - Design Provably More Powerful Graph Neural Networks for Structural Representation Learning. | 2 |
| 10 | 29 | |
| 11 | 10 | |
| 12 | 10 | |
| 13 | 1 | |
| 14 | 0 | |
| 15 | 1 |
About Yanbang Wang
Yanbang Wang is a scholar working on Statistical and Nonlinear Physics, Soil Science and Atomic and Molecular Physics, and Optics, having authored 15 papers that have together received 184 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (5 papers), Quantum optics and atomic interactions (3 papers) and Cold Atom Physics and Bose-Einstein Condensates (3 papers). The work is most often cited by research in Soil Science (25 citations), Artificial Intelligence (60 citations) and Computer Vision and Pattern Recognition (35 citations). Yanbang Wang has collaborated with scholars based in China, United States and Czechia. Frequent co-authors include Jure Leskovec, Liguo Zhang, Qian Jiang, Huamin Qu, Xinhuan Shu, Panming Fu, Ting-Chuen Pong, Xin Mi, Yong Wang and Zhanchun Zuo. Their work appears in journals such as Physical Review A, Agricultural Water Management and Journal of the Optical Society of America B.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.